Action-based collaboration analysis for group learning

In computer-supported collaborative learning, technology is used for enabling constructive approaches to learning as well as group interaction, particularly in distant and asynchronous situations. Recently, shared workspace systems have been developed that allow for jointly constructing conceptual problem representations by means of graphical structures and direct manipulation as well as the incorporation of existing material and the persistent storage of results. Though these systems feature free and open-ended collaboration, they lack any kind of awareness of relevant group interaction aspects. However, an explicit description of collaboration processes is necessary for monitoring and visualizing group interaction, enabling didactic interventions and intelligent support, empirically investigating human collaboration, and evaluating system design. The automatic analysis of user actions in shared workspaces (action-based collaboration analysis) is regarded as a promising new direction. In contrast to discourse-oriented approaches, it especially takes into account the operational grounding of group interactions in the context of collaboratively constructed problem representations. Based on software engineering considerations, a generic plug-in agent architecture has been developed to generally provide operational semantics and intelligent support for different types of user interfaces, particularly shared workspaces. The user interfaces and intelligent components communicate by broadcasting messages that represent the creation, deletion, and modification of objects. Realized as an intelligent component, activity recognition automatically and incrementally infers abstract notions of group activity and interpretations of problem-related conflicts and coordinations from a stream of action messages. The approach, which is related to plan recognition in the situation calculus, has been formalized based on concepts of basic actions and complex, higher-level activities, situations that reconstruct relevant aspects of the user environment, operators that specify a hierarchy of activities, and pending actions that represent potential subsequent action sequences. Activity recognition does not rely on domain and task knowledge, though it can use possibly available information to improve its results. The results of the interaction analysis are visualized in different forms including direct feedback to the shared workspaces. The feasibility of the approach has been verified with a number of test subjects in realistic face-to-face sessions, with activity recognition being used for an analysis-based indexing of video recordings.